Voting Rules As Error-Correcting Codes
Authors: Ariel Procaccia, Nisarg Shah, Yair Zick
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Carnegie Mellon University arielpro@cs.cmu.edu Nisarg Shah Carnegie Mellon University nkshah@cs.cmu.edu Yair Zick Carnegie Mellon University yairzick@cmu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | Mao, Procaccia, and Chen (2013) collected these datasets dots and puzzle via Amazon Mechanical Turk. |
| Dataset Splits | No | The paper describes the datasets and their use in evaluation but does not specify training, validation, or test splits in terms of percentages or counts for model training or tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use the average error in a profile as the bound t given to OPTd, i.e., we compute OPTd(t , π) on profile π where t = d(π, σ ). [...] To synchronize the results across different profiles, we use r = (bt MAD)/(t MAD), where MAD is the minimum average distance of any ranking from the votes in a profile, that is, the average distance of the ranking returned by MINISUMd from the input votes. For all profiles, r = 0 implies bt = MAD (the smallest value that admits a possible ground truth) and r = 1 implies bt = t (the true average error). In our experiments we use r [0, 2]; here, bt is an overestimate of t for r (1, 2] (a valid upper bound on t ), but an underestimate of t for r [0, 1) (an invalid upper bound on t ). |